Neural networks work by mimicking the structure of the human brain to process data and make predictions. They consist of layers of interconnected nodes (neurons) where each connection has an associated weight. Data passes through these layers, and activation functions introduce non-linearity, enabling the network to learn complex patterns.
During training, the network adjusts its weights using backpropagation, a method that calculates the gradient of the loss function with respect to the weights. Optimizers like gradient descent update the weights iteratively to minimize the loss, improving the network's predictions.
Neural networks can be tailored to specific tasks by modifying their architecture. For example, CNNs are effective for image-related tasks, while RNNs handle sequential data like time series or text. The versatility of neural networks makes them suitable for a wide range of applications, from computer vision to NLP.